Software-defined radio linking systems

ABSTRACT

The disclosed invention includes methods for linking individual software-defined radios (SDR) into a cohesive network of SDRs capable of recording a sample of radiofrequency (RF) signals emitted in an RF environment. Individual SDRs communicate with an IP network, and host a linking application that executes the recording. A user identifies a lead SDR from among the SDRs, and uses the lead SDR to task participating SDRs with reference to a clock source. Also disclosed is a system of SDRs configured to be linked into a cohesive network of SDRs capable of recording a sample of RF signals emitted in an RF environment. Embodiments of the disclosed invention include co-located and dispersed SDRs. Some embodiments use SDRs organized into a mesh network. Embodiments of the disclosed invention are configured to perform total band monitoring, total band capture, RF environment simulation, interference identification, interference simulation, and distributed quality of service evaluation of wireless networks.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Application No.63/011,757, filed Apr. 17, 2020; and has specification that builds uponU.S. application Ser. No. 14/265,211, filed Apr. 29, 2014; andPCT/US20/46808, filed Aug. 18, 2020; and PCT/US20/55370, filed Oct. 13,2020; the disclosures of which are hereby incorporated herein byreference in their entirety.

BACKGROUND Field of the Invention

Embodiments of the disclosed invention relate, in general, toSoftware-designed radio (SDR) networks, and more particularly to linkinga plurality of Wide Band Transcorders (WBT) to operate as a singlesystem.

Relevant Background

Software-defined radios, such as wide band transcorders, have inherentlimitations on the amount of Radio Frequency (“RF”) spectrum they canreceive and process. As a result, certain military and civilianapplications would require multiple SDR units with overlapping orcomplementary frequency ranges to capture or playback frequency-agilesignals, to broadcast large RF signatures, e.g., military deceptionoperations “MILDEC”, or to gather a broad view of the RF environment. Acommon approach in the art is to assemble a number of SDRs into a singlelarge rack-mounted system. However, large fixed systems are impracticalas they cannot be quickly deployed to an area of interest. In addition,assembling many SDRs into a single system may not effectively utilizeexisting SDR resources, may be logistically inconvenient or difficult,and for many applications would not have the desired performancecharacteristics.

Therefore, it is apparent that a need exists for a system of dismounted,tactical devices connected via a network that are configured to act asone unified system or a series of smaller systems to allow for broaderuse cases and to enable the units to move where desired (and beseparated by kilometers if using the mesh radio communication approach).These and other deficiencies of the prior art are addressed by one ormore embodiments of the present invention. Additional advantages andnovel features of this invention shall be set forth in part in thedescription that follows, and in part will become apparent to thoseskilled in the art upon examination of the following specification ormay be learned by the practice of the invention. The advantages of theinvention may be realized and attained by means of theinstrumentalities, combinations, compositions, and methods particularlypointed out hereafter.

BRIEF DESCRIPTION OF THE DRAWINGS

Features and objects of the present invention and the manner ofattaining them will become more apparent, and the invention itself willbe best understood, by reference to the following description of one ormore embodiments taken in conjunction with the accompanying drawings andfigures imbedded in the text below and attached following thisdescription.

FIG. 1 depicts a cutaway view of an SDR as used with the disclosedinvention.

FIG. 2 depicts a block diagram of systems comprising an SDR.

FIG. 3 depicts a block diagram of systems comprising an SDR.

FIG. 4 depicts a flow diagram of artificial intelligence-assistedprocesses used by an SDR.

FIG. 5 depicts a flow diagram of artificial intelligence-assistedprocesses used by an SDR.

FIG. 6 depicts a flow diagram of artificial intelligence-assistedprocesses used by an SDR.

FIG. 7 depicts a flow diagram of artificial intelligence-assistedprocesses used by an SDR.

FIG. 8 depicts a flow diagram describing at least a portion of anembodiment of the disclosed invention.

FIG. 9 depicts a flow diagram describing at least a portion of anembodiment of the disclosed invention.

The Figures depict embodiments of the present invention for purposes ofillustration only. One skilled in the art will readily recognize fromthe following discussion that alternative embodiments of the structuresand methods illustrated herein may be employed without departing fromthe principles of the invention described herein.

DEFINITIONS

“Application” or “App” mean programs, code, processes, or operationsstored on a SDR that implement reception and transforms of RF signals,storage of the RF signals, and/or playback of the RF signals.

“App Space” means one or more apps stored on an SDR that may performtransforms of RF signals.

“Artificial Intelligence” (AI) means a branch of computer scienceconcerned with building smart machines capable of performing tasks thattypically require human intelligence. AI is an interdisciplinary sciencewith multiple approaches that allow machines to learn from experience,adjust to new inputs and perform human-like tasks. Using thesetechnologies, computers can be trained to accomplish specific tasks byprocessing large amounts of data and recognizing patterns in the data.

“Intelligent Agent” or “Agent” means a program that can make decisionsor perform a service based on its environment, experiences, and userinput. It is an autonomous entity which acts, directing its activitytowards achieving goals, upon an environment using observation throughsensors and consequent actuators.

“Size, Weight, Power, and Cost” or “SWaP-C” means the hardware footprintof a piece of equipment or system. Swap-C refers to the optimization ofthe four hardware factors as weighed against the capabilities of theequipment or system.

“RF Spectrum” or “Radio Spectrum” means the part of the electromagneticspectrum with frequencies from 30 cycles per second (hertz) to 300billion cycles per second (GHz). Electromagnetic waves in this frequencyrange, called radio waves, are widely used in modern technology,particularly in telecommunications.

“RF Environment” means all of the transmissions in the RF spectrumpropagating through a given geographic area.

“Bandwidth” means a range of frequencies within a given band, inparticular those frequencies used for transmitting a signal.

“Software-defined radio” or “SDR” means a radio communication systemimplemented through software installed on a general or special purposecomputer rather than through traditional hardware components, e.g.,mixers, filters, amplifiers, modulators, signal detectors. A typical SDRmay comprise a personal computer with a sound card linked to an RF frontend. The computer's processor handles substantial portions of the signalprocessing, as opposed to the dedicated circuitry used in traditionalradios. SDR systems are highly flexible as to radio protocols, and mayaccommodate a number of different and changing protocols in real time,e.g., cell phone services. SDRs can transmit using wideband, spreadspectrum, and frequency hopping techniques to minimize interferencewithin an RF environment. Further, a number of SDRs may be linkedtogether into mesh networks reducing power requirements, size andinterference caused by individual nodes in the network.

“Wide-Band Transcorder” or “WBT” means an SDR platform designed torecord and replay any signal within the RF spectrum with a minimalSWaP-C footprint. The platform can be used for real-time or delayedanalysis and manipulation of any signal captured in a particular RFenvironment. An exemplary device is capable of recording up to 100million cycles per second (MHz) of RF spectrum at frequencies between100 thousand cycles per second (kHz) and 18 GHz.

DETAILED DESCRIPTION

The disclosed invention is an application for use on networked SDRs,e.g., WBTs, that allows multiple SDRS to be configured and operated as asingle, highly scalable RF signal recording and playback system. Oneembodiment of the disclosed invention allows for the use of a pluralityof SDRs connected to a network and is configurable to accommodate bothco-located and distributed SDR setups. The disclosed application allowsfor the linking of up to 128 SDRS on a single network so that the SDRsmay be coordinated to accomplish a number of tasks, to include thefollowing: monitoring of the entire band or RF Spectrum being used in anRF environment; recording the entire band or RF Spectrum being used inan RF environment; high fidelity simulation of an RF environment;identification and or simulation of interference sources; and theevaluation of the quality of service of distributed wireless networks.

The disclosed invention will now be described in detail with referenceto several embodiments thereof as illustrated in the accompanyingFigures. In the following description, numerous specific details are setforth in order to provide a thorough understanding of embodiments of thedisclosed application. It will be apparent, however, to one skilled inthe art that embodiments may be practiced without some or all of thesespecific details. In other instances, well known process steps and/orstructures have not been described in detail in order to notunnecessarily obscure the present invention. The features and advantagesof embodiments may be better understood with reference to the drawingsand discussions that follow.

It should be apparent to those skilled in the art that the describedembodiments of the disclosed invention provided herein are illustrativeonly and not limiting, having been presented by way of example only. Allfeatures disclosed in this description may be replaced by alternativefeatures serving the same or similar purpose, unless expressly statedotherwise. Therefore, numerous other embodiments of the modificationsthereof are contemplated as falling within the scope of the presentinvention as defined herein and equivalents thereto. Hence, use ofabsolute and/or sequential terms, such as, for example, “always,”“will,” “will not,” “shall,” “shall not,” “must,” “must not,” “first,”“initially,” “next,” “subsequently,” “before,” “after,” “lastly,” and“finally,” are not meant to limit the scope of the present invention asthe embodiments disclosed herein are merely exemplary.

It will be also understood that when an element is referred to as being“on,” “attached” to, “connected” to, “coupled” with, “contacting”,“mounted” etc., another element, it can be directly on, attached to,connected to, coupled with or contacting the other element orintervening elements may also be present. In contrast, when an elementis referred to as being, for example, “directly on,” “directly attached”to, “directly connected” to, “directly coupled” with or “directlycontacting” another element, there are no intervening elements present.It will also be appreciated by those of skill in the art that referencesto a structure or feature that is disposed “adjacent” another featuremay have portions that overlap or underlie the adjacent feature.

Spatially relative terms, such as “under,” “below,” “lower,” “over,”“upper” and the like, may be used herein for ease of description todescribe one element or feature's relationship to another element(s) orfeature(s) as illustrated in the figures. It will be understood that thespatially relative terms are intended to encompass differentorientations of a device in use or operation in addition to theorientation depicted in the figures. For example, if a device in thefigures is inverted, elements described as “under” or “beneath” otherelements or features would then be oriented “over” the other elements orfeatures. Thus, the exemplary term “under” can encompass both anorientation of “over” and “under”. The device may be otherwise oriented(rotated 90 degrees or at other orientations) and the spatially relativedescriptors used herein interpreted accordingly. Similarly, the terms“upwardly,” “downwardly,” “vertical,” “horizontal” and the like are usedherein for the purpose of explanation only unless specifically indicatedotherwise.

Software Defined Radio Platform

Embodiments of the disclosed SDR linking system may be implemented usingvarious SDRs, as long as the SDRs have sufficient basic processing andmemory capabilities. With reference to FIG. 1 , an example SDR 100 mayinclude a processor 110 paired with internal memory 112. Internal memory112 may be either volatile or non-volatile, and may be encrypted andsecure. The processor 110 may provide output to, and receive input from,a touchscreen display panel 120, such as a resistive-sensingtouchscreen, or the processor may include one or more buttons 140, akeypad, a keyboard, a mouse, or other input devices in addition to, orinstead of, the touchscreen. SDRs used in the disclosed system may haveone or more antennae 130 for transmitting and receiving radiofrequency(RF) signals that may be connected to a wireless data link and/orcellular telephone transceiver 132 coupled to the processor. In additionto the antennae, some SDRs may also include a wired RF connection 134,such as a coaxial cable or other RF input/output feature.

FIG. 2 is a component block diagram illustrating three stages of RFsignal processing on an SDR, respectively performed by a collectionsystem 210, a data storage system 220, and a transmission system 230.The collection system is designed to receive RF signals 211 from the RFenvironment, and includes components to facilitate RF collection, e.g.,filters/amplifiers 213, a digital downconverter 214, ananalog-to-digital (A/D) converter 215, and a collection processor 216.The one or more antennas 212 may receive a wireless analog RF signal 211propagating in the RF environment. In some SDRs, the collection systemis configured to receive RF signals through a wired connection (notshown), such as a coaxial cable. A filters/amplifiers component 213,e.g., a high-pass filter, filters and/or amplifies the collected RFsignal. The filtered/amplified RF signal is then passed to thedownconverter 214 and afterward to the A/D converter 215. The A/Dconverter may use various approaches to convert the analog RF signal toa digital signal. Once converted, the digital RF signal may be passed tothe collection processor 216 for additional manipulation before beingstored in the data storage system 220.

Some SDRs include an application 240 (app) residing in an applicationspace (App Space), wherein the app is configured to implement one ormore of the filters/amplifiers component 213, the digital downconverter214, the A/D converter 215, and the collection processor 216, andthereby perform transforms of the collected RF signal 211. In otherwords, the SDR may perform signal processing through an app that causesone or more of the components to apply various transforms to the analogRF signal as it is collected and readied for storage. For example, theapp 240 may cause the A/D converter 215 to apply offsets and skewingalgorithms that, among other things, adjusts the sampling rate ofdigital signals. Or the app may direct the collection processor 216 tosegment the analog RF signal for easier processing and storage. Inanother example, the app 240 may instruct the downconverter 214 and thefilters/amplifiers component 213 to control the frequencies of areceived analog RF signal. Thus, various apps in the App Space maytransform RF signals throughout signal collection by directing thefunction of one or more of the various collection system components.

In some SDRs, the data storage system includes a data storage component221 that is configured to store the processed RF signals as RF digitaldata (RFD) files 222. The RFD files contain digital representations ofreceived analog RF signals. In some embodiments, RFD file includes anin-phase amplitude measurement of the signal (I) and may also include a90 degree (°) phase-shifted amplitude measurement of the signal (Q). RFDfiles may also contain “meta data” containing relevant information aboutthe RFD files themselves. In some embodiments, an app 240 included inthe App Space performs transformations on the digital signal stored inthe RFD files 222. For example, the app 240 may increase the signal'sgain or may apply other filters, effects, and other transforms. The appmay combine multiple RFD Files into a composite RFD File by adding theprocessed signals together, and may also perform additional signalmanipulation, as required. In addition to the tasks outlined above, theapp 240 may also apply transforms traditionally classified as offlinesignal processing (i.e., signal processing that requires substantialcomputational power, or tasks that are otherwise impractical toimplement in real time).

After processing and storage, the SDR may send the RF signal stored asRFD files 222 by the data storage system 220 to the transmission system230 for playback. The transmission system 230 includes one or more ofthe following: a transmission processor 231, a digital-to-analog (D/A)converter 232, an upconverter 233, filters/amplifiers 234, and one ormore antennas 235 for transmitting an analog signal 236. Thetransmission processor 231 manipulates the digital RF signal usingvarious known techniques. In some embodiments, the transmissionprocessor 231 and the collection processor 216 may be the same (e.g., acentral-processing unit (CPU) or a digital signal processor (DSP). Inother embodiments, the processors, are separate components (e.g., thetransmission processor 231 may be a DSP and the collection processor 216may be a CPU). In other embodiments, the processors are one or morecores in one or more multi-core processors, such as a quad- or dual-coreDSP.

The transmission processor 231 then sends the digital RF signal to theD/A converter 232, which converts the digital RF signal to an analogsignal. The D/A converter then sends the analog RF signal to anupconverter 233, which applies various other transforms to the analogsignal before sending it to a filters/amplifiers component 234. Thefilters/amplifiers component applies additional transforms to the analogsignal which is then sent to the antennas 235 for transmission as an RFsignal 236. In some embodiments, the transmission system 230 uses awired connection (not shown), e.g., a coaxial cable, to transmit theanalog signal.

As discussed with respect to the collection system above, someembodiments of the transmission system include an app 240 that isconfigured to direct one or more of the components of the system,including the transmission processor 231, D/A converter 232, upconverter233, and the filters/amplifiers component 234 to implement transforms tothe stored digital RF signal. The app 240 implements various transformsto alter the stored digital signal and prepare it for transmission as ananalog signal. Thus, depending on the particular embodiment, the apps240 included in the App Space may perform various transforms during thestages of signal processing. In particular, the apps 240 applytransforms in the respective systems performing aspects of signalprocessing, i.e., the collection system 210, the data storage system220, and the transmission system 230. In some embodiments, the appsperform transforms on the RF signals without altering the raw ororiginal RF signal data, but in other embodiments, the apps do notpreserve the original RF signal data when applying transforms.

FIG. 3 depicts multiple systems included in an SDR of the disclosedsystem. The SDR may include a central processor (CPU) that has adedicated system area 350, having a higher priority/security than otherSDR systems. The system area 350 may include an operating system drive(OS Drive) 353, that functions as a high-level operating system and thatinteracts with and manages various SDR hardware and software systems.The system area 350 may also include random access memory (RAM) 354, anda microprocessor without interlocked pipelined stages (MIPS) 355, aswell as a system controller 352 to operate the collection system 310,the data storage system 320 and the transmission system 330, and adisplay controller 352 to operate a display screen 360. The CPU systemarea 350 also has an App Space 341, which includes allotted space 343for specific apps, e.g., the linking app 340. The space allotted to eachapp includes the app itself 340 located in the OS Drive 353, RAM 344allocated to the app, and MIPS 345 allocated to the app. Arrows indicatetwo-way 12 and one-way 14 data flows.

A typical SDR, such as a WBT, used with the disclosed system, may havevarious hardware components to implement the systems for collecting,processing, storing and transmitting RF signals. For example, dataprocessing is supplied by 8 CPU cores, 512 GPU cores, and 32 GB of RAM.A combined ARM and GPU board, such as those sold by NVIDIA Corp. performDSP and AI/ML functions. At least two tuners having a sample rate of 50MHz (61.44 MS/s) are used for tuning the signal. Center frequency range(Threshold) is 50-6000 MHz.

Artificial Intelligence Systems for Signal Detection and Classification

Certain functions of the disclosed invention are carried out by a systemof intelligent agents that continually optimizes resources toprogressively refine detection and characterization of radiofrequencysignals in a given environment. The AI/ML systems used for signaldetection and classification herein develop a data repository of an RFenvironment by extracting features from collected observations on theenvironment. Then the systems allow a suite of hierarchically organizedintelligent agents to access the data repository to perform signaldetection and classification. The signal classification system initiallyseparates emitters from each other based on the characteristics ofindividually detected pulses, and then classifies the emitter groupsusing measured characteristics of each collection of pulses. These AIsystems reduce the data burden and training time required to makefieldable, effective, and highly accurate signal detection capabilities,especially for traditionally difficult-to-capture protocols such ascertain frequency agile communications. Using these systems, smallSWaP-C footprint equipment can successfully detect, identify andclassify a new communications protocol in a matter of minutes.

With reference to FIG. 4 , for signal detection and classification, suchsystems use predetermined protocols to deconstruct or parse well-definedqueries 410. A deconstruction agent 460 analyzes an inquiry and parsesit into one more sub-questions 420, depending on the query's complexity.Then the deconstruction agent 460 passes each sub-question to a primaryagent 470 based the nature of the sub-question. Examining data stored ina common database, the primary agent 470 assesses whether the currentdata is sufficient to resolve the sub-question or if additional data isrequired. If more information is needed, the primary agent seeksassistance from one or more secondary agents 480. Like the primaryagent, the secondary agent receives the request and determines whethersufficient information is available to produce its response. Should thesecondary agent also need additional information a tertiary agent can besought, and so forth, until each sub-question is resolved. Uponresolution of each sub-question each primary agent passes its responseback to the deconstruction agent, which combines the responses andresolves the inquiry in the form of an output.

With reference to FIG. 5 , an AI/ML system as used herein is depicted.The intelligent agents, i.e., deconstruction 560, primary 570,secondary, 580, tertiary (not shown), etc. are communicatively coupledto a database. Sensors 520 of various types capture and house data 525relevant to a particular examination. The deconstruction agent 560receives an inquiry 510 and parses it based on a predetermined protocol,which provides a framework to interpret the question and determine whatinformation is necessary and what steps 530 (goals) are required torespond to the query. If the current state of the data 535 is sufficient552 to support the required actions 540, the system generates a responseand reports an output. However, if one or more actions/sub-questionscannot be completed 554 with current data, the deconstruction agent 560seeks assistance of other agents 570, 580. In doing so thedeconstruction agent assigns each sub-question or action to one or moreprimary agents 570. The primary agent 570 examines available data anddetermines whether the data is sufficient to resolve the sub-question.If so, the primary agent produces an output and returns it to thedatabase. If the data is insufficient to resolve the sub-question, theprimary agent 570 engages one or more secondary agents 580. Each ofthese secondary agents are tasked to develop or generate the dataidentified as lacking by the primary agent 570. Should the secondaryagent 580 also determine that available data is insufficient for it tocomplete its task, it can task tertiary agents to generate missing datato allow the secondary agent to respond to the primary agent's request.

As agents produce additional data in response to a need identified by asuperior agent, the output is placed in a common data repository 525.Each agent monitors the data repository for material sufficient for itto complete its assigned question. Upon an inferior agent adding newmaterial to the database the superior agent recognizes the inclusion andworks on its assigned task. Ultimately, the hierarchal structureprovides sufficient information within the database for generation of aresponsive output to the original query 510.

Specifically as applied to RF signal detection and classification,agents automatically detect signals, extract them, put them into adatabase and then accesses that database to provide relevant andactionable information. The agents rapidly assemble the pieces needed toprovide relevant decision support information, e.g., is a signaldetected, where and when was the signal detected, what class of signalis it, and in some cases, what vehicles are associated with that signal.Sensors, e.g., antennas, gather data that may otherwise resemble noise.Indeed, some frequency agile systems are designed to resemble noise by“hopping” among frequencies at a rate exceeding 80,000 hops per second.In such a system, spectrum accumulation and statistical fingerprintanalysis models, are used to provide frequency estimates of RF signals.Such models, or predetermined protocols, establish what data is neededto determine if, for example, a UAV is present in the environment, andrespond to such an inquiry.

The AI/ML systems used herein perform RF signal detection andclassification primarily through two algorithms: Density Based SpatialClustering of Applications with Noise (“DBSCAN”), and Convex Hull. SeeFIG. 6 for a summary of the process as a block diagram. DBSCAN is ahighly effective algorithm for identifying and segregating noise, aswell as performing segmenting analysis on signals. DBSCAN operates byfinding a finite number of spatially dense clusters in an n-dimensionalfeature space by constructing groups out of points that are in closeproximity. DBSCAN is superior to other comparable clustering algorithms,e.g., k-means, because the number of groups do not need to be specifiedbeforehand, and it can find non-linearly separable clusters. If DBSCANcannot assign a point to a cluster, it initially labels the point asnoise, i.e., unclassified.

The second such algorithm, known as Convex Hull, determines the minimumnumber of points in a cluster that can form a closed bounding regionaround all of the points in that cluster. A convex hull can be foundalgorithmically for a cluster of any number of dimensions. Convex hullsrepresent cluster boundaries, serving as a decision surface forestablishing class membership. By calculating the convex hull boundaryfor a cluster, the system no longer requires all of the interior pointsassociated with the cluster, which reduces the amount of memory requiredfor storage and reduces the number of calculations needed to locate anew point inside or outside a cluster's boundary.

In operation, the AI/ML system receives data containing one or morefeatures over a sliding window of time. This time window is large enoughto allow a sufficient number of data elements or detections to allow theformation of meaningful clusters. The number of features passed into thesystem determines the number of dimensions in the feature space.

The system then tests each feature or data point against the convexhulls it has constructed from the data. If a point lies inside of aconvex hull, the point is deemed a member of the class that the convexhull represents. The system can determine a confidence value for thepoint by examining the distance between the point and the convex hullsurface, as well as the distance between the point and the center pointof the cluster. Both distance measurements figure into the confidencevalue, since one convex hull can envelope another convex hull.

If a point lies outside of all convex hulls, it is stored in a slidingwindow of unclassified points. The unclassified points are then runthough a DBSCAN clustering process to determine if there are any denseregions of points sufficient to create one or more new clusters. Theseclusters of unclassified points represent new classes (potentialsignals) that are added the model of the RF environment. Once a newcluster is established, i.e., shows persistence by including a minimumquantity of points during a given timespan, the system generates aconvex hull out of the cluster's surface, and the new convex hull isassigned a name, either by the system, an auto generated uniquedesignator, or by a human user.

Once a new class is established, the representative convex hull is addedto the model of the RF environment, and is communicated to otherintelligent agents residing in other SDRs through a transfer learningprocess. The DBSCAN and convex hull algorithms make such communicationand learning efficient by creating small model files (KB rather than MB)that can be easily transferred between SDRs over low bandwidthcommunications systems, e.g., a mesh network. Transfer learning allowstwo or more agents to exchange classes and improved models amongthemselves, or to share or combine model construction tasks. Bymultiplying the processing power available to create and improve signalmodels, transfer learning allows multiple SDRs to learn from a given RFenvironment in real time. Transfer learning may be performed andorchestrated among networked SDRs either manually, automatically,through the network, or a combination of such organizational means.Transfer learning between models and SDRs is as simple as appending thenew convex hull in one model to the set of convex hulls in anothermodel.

While in some cases the system may require the generation of newinformation to answer an inquiry, in other cases the sensor-collecteddata is sufficient to answer the inquiry, but is not stored in a formusable by the system. Thus, when a query is issued, “Is a frequencyagile UAV operating in a certain region of interest”, it must be parsedinto resolvable and actionable sub-questions. The AI/ML system parsesthe question into actionable sub-questions using such protocols as“Within the detected data, are there groupings of time correlatedsignals”. To answer the query, an agent may need to examine pulselengths, signal bandwidth, pulse power, etc., and analyze the data toidentify a geospatial location of the signals. A response from each of aplurality of sub-questions leads to a resolution of the original query.

For example, with reference to FIG. 7 , a subquery is assigned to aprimary agent, such as a classification agent 770, to identifytime-correlated grouped signals. The classification agent 770,recognizes that time-correlated grouped signals 735 are indicative of afrequency agile system. The classification agent therefore needs, andseeks to determine, whether the signal database 725 includestime-correlated grouped signals. Upon querying the database 725, theprimary agent determines that the stored data are pulses 733 and are nottime correlated. The primary agent 770 then issues a request to asecondary agent, a pulse processor feature extractor 780, to generatetime correlated grouped signals 735 based information present in thedatabase. The secondary agent 780 then seeks pulse start time kerneldensities and signal time extents with which it can determine pulsestart times 737. While pulse start time kernel densities can be derivedfrom the signal time extents, the signal time extents must be derivedfrom power spectral data, and Signal Frequency Extents which is anoutput of a complex Fast Fourier Transform. Working backward with aplurality of tertiary agents, a complex FFT modifies the original data731 to generate power spectral data which in turn feeds the generationof, among other things, signal time extents. The signal time extents arethe basis of another agent's generation of pulse start time kerneldensity data which, when combined with the signal time extents, formspulse start time correlations 737.

Because of the tertiary agents' activities, the database now includespulse start times 737 which the secondary agent 780 can use to generatetime correlation-based signal groupings 735. These groupings aregenerated and placed in the database 725. After the inferior agentscomplete their tasks, the primary classification agent 770 now has thedata (time correlation-based signal groups 735), required to return tothe deconstruction agent a response to the subquery, “Have frequencyagile signals been detected”. This response combined with otherinformation can be combined to output a response to the initial query,“Is a frequency agile UAV operating in a certain region of interest?”

SDR Linking

With reference to FIG. 8 , a flow diagram of a linked system of SDRs isdepicted. A number (three are depicted) of SDRs 810, 812 are deployed inan RF environment. Each SDR is connected to, and configured tocommunicate with, a network 850 organized based on one or more Internetcommunications protocols, e.g., Internet Protocol (IP), TransmissionControl Program (TCP), User Datagram Protocol (UDP), etc. The network850 may be a wired connection, e.g., an ethernet connection, or may bewireless, e.g., WiFi, cellular. In some embodiments, the network 850 isa mesh network, in which network nodes, i.e., SDRs, connect directly,dynamically and hierarchically to as many other nodes as possible, andcooperate to efficiently route data throughout the network. Each SDRconfigured to participate in a linked network of the disclosed inventionhosts a linking application 840 designed to operate each SDR as part ofa cohesive system of SDRs. Additional prior coordination is not requiredto set up the system: as long as the SDR 810, 812 is communicating withan IP network and hosts the linking application 840, it can participatein the system, with an upper limit of 128 SDRs participating at onetime.

A user operating the linked system identifies a Lead SDR 810 that isdeployed in the RF environment the user desires to sample. The Lead SDRis does not require different capabilities from other SDRS on thenetwork, and may be any SDR that is connected to the network and hoststhe linking app. Once designated, the Lead SDR acts as a command andcontrol station for the linked system. The user controls the linkedsystem through a graphical user interface displayed by a general orspecial purpose computer that is also connected to the network.Communication with the Lead SDR is through the network, and is routedthrough one or more network switches 830 (one is depicted).

The user instructs the Lead SDR to record RF signals in the RFenvironment to be sampled. Such instructions include a sample starttime, being the time at which the user wants to begin recording the RFenvironment, and a sample stop time, being the time at which the userwants to cease recording the RF environment. Other user instructions mayvary in detail, and may include the RF spectrum to be covered, a centerfrequency, a bandwidth, and a sample rate. Each individual SDR 810, 812has a bandwidth that it is capable of covering, for example 100 MHz ofRF spectrum. Acting in concert, the disclosed system aggregates theindividual SDR bandwidths into a system frequency band of RF spectrum.This system frequency band is a subset of frequencies between, forexample, 100 kHz and 40 GHz, and is up to 2 GHz wide. In other words,the combined coverage of the individual SDRS has an instantaneousbandwidth of 2 GHz. As SDR capabilities evolve, individual SDRbandwidths, aggregate frequency range, and instantaneous bandwidths willincrease.

Once the Lead SDR is tasked by a user, the linking application 840establishes a reference time using a clock signal generator 820. Theclock signal generator may be internal to the SDR, may be a clock signalgenerator connected to the network, or may be a clock signal from a GPSsatellite or other broadcast source. Without necessarily knowing whatSDR resources are available, and without prior coordination, the LeadSDR communicates the reference time and recording instructions to thenetwork 850. The Lead SDR communicates through multicast messages sentwith the user datagram protocol (UDP). The model for UDP communicationsis simple, connectionless, and requires few protocol mechanisms. Forexample, prior communications are not required to establish data paths,such paths can occur instantaneously with low bandwidth messaging. Usingsuch protocols, the Lead SDR is able to perform command and controlfunctions over member SDRS 812 in the linked system using a datathroughput of as little as 75 thousand bits per second (Kbps).

SDRs 812 on the network 850 join the linked system through anauto-discovery process. Member SDRs on the same communications channelas the Lead SDR will receive the UDP message even if the two units hadnot communicated previously. Once the Lead SDR sends the time-stampedrecording instruction over UDP multicast, all of the SDRs on the networkwould receive the message, and if loaded with the linking application,would coordinate timing and record the RF spectrum as instructed. Thenetwork auto-discovery feature enabled by use of UDP communicationsprotocols and the disclosed linking application provides importantoperational advantages, including the ability to rapidly coordinate theavailable SDRs for participation in the network with only minimalknowledge of the configuration of the network, and minimal knowledge ofSDR configuration. Namely, if an SDR is connected to a network with thelead SDR and is loaded with the application, it is available to betasked as part of the linked network. The user receives feedback throughthe graphical user interface about the particular SDRS on the networkthat join the linked system for a given recording task, as well as whenthe participating SDRS recorded, and what they recorded.

Synchronization of recording start times and stop times is accomplishedthrough use of the reference time communicated by the Lead SDR 810. Thelinking application operating on each member SDR 812 uses the referencetime to synchronize each member SDR's internal clock with the Lead SDRso that recordation is precisely coordinated. The clock source 820broadcasts a pulse per second (PPS) signal to the network 850. The PPSis a signal with a width of less than a second, and is characterized bya sharp or abrupt edge that repeats each second. The Lead SDR, throughthe linking application, selects a time that is offset, for example,until the next full PPS signal from the clock source, and uses this timeas the reference time. By offsetting the reference time until the nextfull PPS signal, the Lead SDR ensures that member SDRS begin and ceaserecording with precise coordination.

With reference to FIG. 9 , another embodiment of the disclosed SDRlinking system is depicted. In this embodiment, rather than using aclock source that communicates through the Lead SDR 910, the SDRS 912connected to the network 950 also include a Global Positioning System(GPS) antenna 922 configured to communicate with one or more GPSsatellites 920. The SDRs 910, 912 use the clock on board the GPSsatellite 920 as a reference time for coordinating RF environmentalsampling start times and stop times.

The disclosed invention allows for a plurality of SDRs to be combinedinto a single system to simultaneously record RF signals in an RFenvironment to be played back, or to be stored for later signal analysisand processing. Using multiple SDRs for RF environment sampling providestwo main advantages. Since an individual SDR has a limited frequencyrange, and limited instantaneous bandwidth, e.g., 100 MHz, combiningmultiple SDRs with different or partially overlapping frequency rangesallows coverage of all frequencies, for example, between 100 kHz and 40GHz, and provides, e.g., up to 2 GHz of instantaneous bandwidth. Suchcapabilities typically require large rack-mounted systems costing inexcess of $1 million each. The combined capabilities of a plurality ofSDRs linked according to the disclosed invention therefore allows a userto record all, or nearly all, of the RF emissions in a given RFenvironment. Further, the small SWaP-C footprint of an individual SDR,and the ability to use distributed SDRs located throughout an RFenvironment, provides superior flexibility and geographic coverage forRF environment sampling. Further, dismounted systems allow for broaderuse cases since individual SDRs can be deployed and moved where needed,and separated by distances in the multiple kilometer range. IndividualSDRS may be co-located at a single location, or at least a subset ofSDRs can be distributed at regular or irregular intervals around thegeographic area selected for coverage. SDRS participating in the linkedsystem may have a fixed location, may be portable, or mounted on avehicle for full mobility.

The disclosed linked network of SDRs is capable of performing a numberof functions due to the substantial coverage of RF spectrum, as well asthe instantaneous bandwidth available. Such functions include total bandmonitoring, wherein the linked system analyzes in real time all of theRF signals emitted into the RF environment in a unit of time. Disclosedlinked systems can also perform total band capture, wherein the systemrecords all of the RF signals emitted into the RF environment in a unitof time. The RF spectrum coverage provided by a linked system alsoallows the creation of accurate and comprehensive simulations of an RFenvironment. Another application is the identification and simulation ofelectronic interference, electronic noise, intentional jamming, etc.that might be present in an RF environment. Such interference and noisedetection is made possible by the linked system's ability to capture andrapidly analyze all of the RF signals in an environment, so that noise,interference and or jamming may be distinguished from communicationssignals. Finally, the disclosed linked system allows distributed qualityof service evaluations or wireless networks, such as cellular networks.

Some portions of this specification are presented in terms of algorithmsor symbolic representations of operations on data stored as bits orbinary digital signals within a machine memory (e.g., a computermemory). These algorithms or symbolic representations are examples oftechniques used by those of ordinary skill in the data processing artsto convey the substance of their work to others skilled in the art. Asused herein, an “algorithm” is a self-consistent sequence of operationsor similar processing leading to a desired result. In this context,algorithms and operations involve the manipulation of informationelements. Typically, but not necessarily, such elements may take theform of electrical, magnetic, or optical signals capable of beingstored, accessed, transferred, combined, compared, or otherwisemanipulated by a machine. It is convenient at times, principally forreasons of common usage, to refer to such signals using words such as“data,” “content,” “bits,” “values,” “elements,” “symbols,”“characters,” “terms,” “numbers,” “numerals,” “words”, or the like.These specific words, however, are merely convenient labels and are tobe associated with appropriate information elements.

Unless specifically stated otherwise, discussions herein using wordssuch as “processing,” “computing,” “calculating,” “determining,”“presenting,” “displaying,” or the like may refer to actions orprocesses of a machine (e.g., a computer) that manipulates or transformsdata represented as physical (e.g., electronic, magnetic, or optical)quantities within one or more memories (e.g., volatile memory,non-volatile memory, or a combination thereof), registers, or othermachine components that receive, store, transmit, or displayinformation.

An exemplary system for implementing the invention may include a generalpurpose computing device such as the form of a conventional personalcomputer, a personal communication device or the like, including aprocessing unit, a system memory, and a system bus that couples varioussystem components, including the system memory to the processing unit.The system bus may be any of several types of bus structures including amemory bus or memory controller, a peripheral bus, and a local bus usingany of a variety of bus architectures. The system memory generallyincludes read-only memory (ROM) and random-access memory (RAM). A basicinput/output system (BIOS), containing the basic routines that help totransfer information between elements within the personal computer, suchas during start-up, is stored in ROM. The personal computer may furtherinclude a hard disk drive for reading from and writing to a hard disk, amagnetic disk drive for reading from or writing to a removable magneticdisk. The hard disk drive and magnetic disk drive are connected to thesystem bus by a hard disk drive interface and a magnetic disk driveinterface, respectively. The drives and their associatedcomputer-readable media provide non-volatile storage of computerreadable instructions, data structures, program modules and other datafor the personal computer. Although the exemplary environment describedherein employs a hard disk and a removable magnetic disk, it should beappreciated by those skilled in the art that other types of computerreadable media which can store data that is accessible by a computer mayalso be used in the exemplary operating environment.

Embodiments of the present invention as have been herein described maybe implemented with reference to various wireless networks and theirassociated communication devices. Networks can also include mainframecomputers or servers, such as a gateway computer or application server(which may access a data repository). A gateway computer serves as apoint of entry into each network. The gateway may be coupled to anothernetwork by means of a communications link. The gateway may also bedirectly coupled to one or more devices using a communications link.Further, the gateway may be indirectly coupled to one or more devices.The gateway computer may also be coupled to a storage device such asdata repository.

While there have been described above the principles of the presentinvention in conjunction with linked SDRs, it is to be clearlyunderstood that the foregoing description is made only by way of exampleand not as a limitation to the scope of the invention. Particularly, itis recognized that the teachings of the foregoing disclosure willsuggest other modifications to those persons skilled in the relevantart. Such modifications may involve other features that are alreadyknown per se and which may be used instead of or in addition to featuresalready described herein. Although claims have been formulated in thisapplication to particular combinations of features, it should beunderstood that the scope of the disclosure herein also includes anynovel feature or any novel combination of features disclosed eitherexplicitly or implicitly or any generalization or modification thereofwhich would be apparent to persons skilled in the relevant art, whetheror not such relates to the same invention as presently claimed in anyclaim and whether or not it mitigates any or all of the same technicalproblems as confronted by the present invention. The Applicant herebyreserves the right to formulate new claims to such features and/orcombinations of such features during the prosecution of the presentapplication or of any further application derived therefrom.

While this invention has been described in terms of several embodiments,there are alterations, modifications, permutations, and substituteequivalents, which fall within the scope of this invention. Althoughsubsection titles have been provided to aid in the description of theinvention, these titles are merely illustrative and are not intended tolimit the scope of the present invention. In addition, where claimlimitations have been identified, for example, by a numeral or letter,they are not intended to imply any specific sequence.

It should also be noted that there are many alternative ways ofimplementing the methods and apparatuses of the present invention. It istherefore intended that the following appended claims be interpreted asincluding all such alterations, modifications, permutations, andsubstitute equivalents as fall within the true spirit and scope of thepresent invention.

This has been a description of the disclosed invention along with apreferred method of practicing the invention, however the inventionitself should only be defined by the appended claims.

What is claimed is:
 1. A method for linking individual software-defined radios (SDR) into a system of SDRs, the method comprising: configuring a plurality of SDRs to communicate with a network, wherein each of the plurality of SDRs is a software-defined radiofrequency (RF) digitization and collection device designed to record and play back signals in a band of RF spectrum, and wherein the network is organized based on one or more Internet communications protocols; hosting on each of the plurality of SDRs a linking application (app) designed to operate each SDR as part of a system; identifying a lead SDR from among the plurality of SDRs; directing the lead SDR to record a sample of RF signals in an RF environment, wherein the linking app hosted on the lead SDR establishes a reference time using a clock signal generator, and sends a message to the network to record the sample in coordination with the reference time; and recording the sample in coordination with the reference time, wherein a subset of the plurality of SDRs joins the system automatically to record the sample.
 2. The method of claim 1, wherein the linking app hosted on the lead SDR directs the linking apps hosted on the subset of SDRs.
 3. The method of claim 1, wherein the method further comprises communicating with the lead SDR through a network switch.
 4. The method of claim 1, wherein the clock signal generator is one of the following: one or more global positioning system (GPS) satellites, a clock source connected to the network, or a clock source internal to an SDR.
 5. The method of claim 1, wherein the plurality of SDRs is co-located.
 6. The method of claim 1, wherein one or more of the plurality of SDRs is mobile.
 7. The method of claim 1, wherein the network is a wireless mesh network.
 8. The method of claim 1, wherein the lead SDR controls the system using a data throughput of 75 thousand bits per second (Kbps) or more.
 9. The method of claim 1, wherein the band of RF spectrum recorded by each of the subset of SDRs is 100 million cycles per second (MHz) wide.
 10. The method of claim 1, wherein the bands are aggregated into a system frequency band, and wherein the system frequency band is a subset of radiofrequencies between 100 thousand cycles per second (kHz) and 40 billion cycles per second (GHz).
 11. The method of claim 10, wherein the system frequency band is 2 GHz wide.
 12. The method of claim 1, wherein the system is configured to perform one or more of the following: total band monitoring, total band capture, RF environment simulation, interference identification, interference simulation, and distributed quality of service evaluation of wireless networks.
 13. A system for recording radiofrequency (RF) transmissions using a linked group of SDRs, wherein the lead SDR being directed to record a sample of RF signals in an RF environment, the system comprising: a plurality of SDRs, wherein each of the plurality of SDRs is a software-defined radiofrequency (RF) digitization and collection device designed to record and play back signals in a band of RF spectrum, wherein each of the plurality of SDRs hosts a linking application (app) for operating the linked group, and wherein one of the plurality of SDRs is identified as a lead SDR; a network organized based on one or more Internet communications protocols, wherein the plurality of SDRs communicates with the network; a clock signal generator configured to communicate with the lead SDR; and a network switch for communicating with the lead SDR, wherein the linking app hosted on the lead SDR establishes a reference time using the clock signal generator, and sends a message to the network to record the sample in coordination with the reference time, and wherein the sample being recorded in coordination with the reference time by the plurality of SDRs on the network, and wherein a subset of the plurality of SDRs joins the system automatically to record the sample.
 14. The system of claim 13, wherein the linking app hosted on the lead SDR directs the linking apps hosted on the subset of SDRs.
 15. The system of claim 13, wherein the clock signal generator is one of the following: one or more global positioning system (GPS) satellites, a clock source connected to the network, or a clock source internal to an SDR.
 16. The system of claim 13, wherein the plurality of SDRs is co-located.
 17. The system of claim 13, wherein one or more of the plurality of SDRs is mobile.
 18. The system of claim 13, wherein the network is a wireless mesh network.
 19. The system of claim 13, wherein the lead SDR controls the linked group using a data throughput of 75 thousand bits per second (Kbps) or more.
 20. The system of claim 13, wherein the band is 100 million cycles per second (MHz) wide.
 21. The system of claim 13, wherein the band recorded by each of the subset of SDRs a group frequency band, and wherein the group frequency band is a subset of radiofrequencies between 100 thousand cycles per second (kHz) and 40 billion cycles per second (GHz).
 22. The system of claim 21, wherein the group frequency band is 2 GHz wide.
 23. The system of claim 13, wherein the system is configured to perform one or more of the following: total band monitoring, total band capture, RF environment simulation, interference identification, interference simulation, and distributed quality of service evaluation of wireless networks. 